The present exemplary embodiment relates to device operation where the device can exist in a plurality of modes, each consuming a different amount of energy. It finds particular application in conjunction with the prediction of time-outs for devices, such as printers, and will be described with particular reference thereto.
Imaging devices, such as printers and copiers, operate at different power consumption levels. In an idle mode, the device is ready to use for printing or copying, which typically requires most power. When not in use, the device is cycled down to a power saving mode. These modes are sometimes known as standby modes, low power modes, or sleep modes. In the power saving mode, the device draws enough power to support certain functions of the device, but requires a warm up period before it is fully operation again. The warm up period is actuated, for example, when a print job is received for printing or a user selects a copying function or otherwise actuates the device. The device control system then activates components that draw additional power in preparation for use of the device. For example, a printer or copier may heat a fuser roll and cause the marking material to be readied for use. In the case of laser printers, this generally involves circulating the toner particles in a developer housing. For solid ink printers, the solid inks are heated to above their melting points.
Once the device has been used, it may remain in an idle mode at the higher power consumption level for some predetermined period of time (a time-out), to maintain one or more components within an operational temperature range or state. The time-out reduces the number of cycles experienced by the components, which helps preserve their operational life, and also reduces or eliminates waiting time for the customer. If the device is not in use again by the preset time-out, the device begins to cycle down to the power saving mode.
Currently, in most printers the inactivity period to wait before entering into sleep mode is either set by the administrator or predefined by the device manufacturer according to environmental standards as Energy Star. Until 2006, imaging equipment was evaluated as Energy Star compliant on the basis of having the manufacturer respecting the recommendations of time-outs defined by the EPA, which were dependant on the type of device (e.g., scanner, copiers, multi-function devices) and its speed capabilities. These time-outs were arbitrary and not self-adapted by any logic or intelligence embedded on devices in order to bring further power consumption improvements. Today, Energy Star criteria are based on the evaluation of power consumption during a fixed period of a week in which the imaging equipment receives requests with a predefined standard usage pattern. The result of the evaluation method is the Typical Energy Consumption (TEC) value measured in kWh and which must be under a certain level in order to obtain the Energy Star certification. For example, for a color multi-function device producing under 32 images per minute, its power consumption must be below (0.2 kWh*ipm)*+5 kWh. Although the current evaluation method takes into account a usage pattern, it does not take into account the stochastic nature of usage patterns.
In order to respect TEC maximum levels, manufacturers configure printer time-out strategies reducing the time-out values and making improvements in the energy consumption of print engines. However, time-out values are still static values which, in most cases, are not adapted to the real usage of devices.
Methods for determining a time-out can be summarized as follows: if a device stays in idle mode during a predefined time s then it will enter into sleep mode and stay in this mode until the next incoming request. To find s, several strategies have been proposed. Lu and Micheli adjust s according to the unavailability time of the device due to the fact of switching from sleep to an active status (Y. Lu and G. De Micheli. Adaptive hard disk power management on personal computers. IEEE Great Lakes Symposium on VLSI, pages 50-53, 1999). Douglis, et al. set s according to the relation between the idle period and the wakeup delay (time to get out of sleep mode). If this relation is small, s increases and s decreases otherwise. (F. Douglis, P. Krishnan, and B. Bershad. Adaptive disk spin-down policies for mobile computers. In Proc. 2nd USENIX Symp. on Mobile and Location-Independent Computing, 1995).
Parametric approaches for determining s try to fit user behavior and more in particularly the inter-arrival time between two subsequent jobs or print requests distribution to a parametric model and extract the parameters which best fit the model. Such approaches can yield inaccuracies due to the difficulty in fitting parameters to distributions, such as Weibull or Normal distributions to real usage behavior.
Therefore a method for inferring a time-out is desired which avoids these problems.
Incorporation by Reference
The following reference, the disclosure of which is incorporated herein in its entirety by reference, is mentioned.
U.S. Pub. No. 20080109663, published May 8, 2008, entitled SYSTEM AND METHOD FOR REDUCING POWER CONSUMPTION IN A DEVICE, by Snyder, et al., discloses a system and process for enabling a device to adjust the duration of various power modes based on usage of the device. The process includes operating the device at a fully operational power level, counting a first wait time, modifying a first wait time modifier in response to detection of image generating device use prior to expiration of the first wait time, and reducing power consumption from the fully operational level to a low power level in response to expiration of the first wait time.
Brief Description
In accordance with one aspect of the exemplary embodiment, a method of computing a time-out for a device includes acquiring data comprising a set of inter-arrival times for at least one device. This set of inter-arrival times values can be examined as the set of candidate time-outs. For each of a set of candidate time-outs, the method includes deriving a probability that an inter-arrival time from the set of inter-arrival times is greater than the candidate time-out. A cost function is computed, e.g., with a computer processor, based on the derived probability and a robustness term and a time-out identified for the at least one device as the one minimizing the cost function value.
In another aspect, a computer implemented system for computing a time-out for a device includes data memory which stores acquired data comprising a set of inter-arrival times for at least one device, main memory which stores instructions which, for each of a set of candidate time-outs, derive a probability that an inter-arrival time from the set of inter-arrival times is greater than the candidate time-out, compute a cost function based on the derived probability and a robustness term, and identify a time-out for the at least one device for which the cost function is a minimum, and a processor in communication with the main memory which executes the instructions for processing the acquired data.
In accordance with another aspect, a printing system includes a plurality of networked printers which each acquire inter-arrival data for print jobs. A time-out system receives the inter-arrival data from the printers, generates at least one histogram therefrom for a set of candidate time-outs, and computes a time-out for the plurality of printers by minimizing a cost function, the cost function including a robustness term which factors in adversarial behavior not included in the histogram.